Many companies and other organizations operate computer networks that interconnect numerous computing systems to support their operations, such as with the computing systems being co-located (e.g., as part of a local network) or instead located in multiple distinct geographical locations (e.g., connected via one or more private or public intermediate networks). For example, data centers housing significant numbers of interconnected computing systems have become commonplace, such as private data centers that are operated by and on behalf of a single organization, and public data centers that are operated by entities as businesses to provide computing resources to customers. Some public data center operators provide network access, power, and secure installation facilities for hardware owned by various customers, while other public data center operators provide “full service” facilities that also include hardware resources made available for use by their customers. However, as the scale and scope of typical data centers has increased, the tasks of provisioning, administering, and managing the physical computing resources have become increasingly complicated.
The advent of virtualization technologies for commodity hardware has provided benefits with respect to managing large-scale computing resources for many customers with diverse needs, allowing various computing resources to be efficiently and securely shared by multiple customers. For example, virtualization technologies may allow a single physical computing machine to be shared among multiple users by providing each user with one or more virtual machines hosted by the single physical computing machine, with each such virtual machine being a software simulation acting as a distinct logical computing system that provides users with the illusion that they are the sole operators and administrators of a given hardware computing resource, while also providing application isolation and security among the various virtual machines. Furthermore, some virtualization technologies are capable of providing virtual resources that span two or more physical resources, such as a single virtual machine with multiple virtual processors that spans multiple distinct physical computing systems. As another example, virtualization technologies may allow data storage hardware to be shared among multiple users by providing each user with a virtualized data store which may be distributed across multiple data storage devices, with each such virtualized data store acting as a distinct logical data store that provides users with the illusion that they are the sole operators and administrators of the data storage resource. Virtualization may be implemented at multiple levels—for example, some cloud computing vendors may provide a potentially large collection of networked resources to a given client as a “virtual private cloud”, such that to the client, the set of networked resources appears to be an isolated private network in which the client has almost as much administrative control (e.g., with respect to network addressing, routing, and so on) as if the resources all resided within the client's own data center.
In many environments, operators of large provider networks that implement different types of virtualized computing, storage, and/or other network-accessible functionality also support various kinds of enhanced services, e.g., easy to use load balancing mechanisms, clustered resources that can be dedicated to certain classes of applications such as map-reduce or other distributed computing applications, workflow services, specialized software development and deployment systems, and so on. Many of these enhanced services are often implemented somewhat independently of one another, for example by different development groups at different points in time, potentially with different design goals and implementation techniques, which can result in inefficiencies when certain types of operational or administrative questions need to be answered. For example, depending on the different roles that compute instances of the network may play in providing the various types of enhanced services, there may be multiple different information sources that can be used in several combinations to answer a question of the type “Which compute instances in the network have version V1 of software S1 installed?.” A simplified, unified approach to such resource identification problems may help enhance operational productivity.
While embodiments are described herein by way of example for several embodiments and illustrative drawings, those skilled in the art will recognize that embodiments are not limited to the embodiments or drawings described. It should be understood, that the drawings and detailed description thereto are not intended to limit embodiments to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope as defined by the appended claims. The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include,” “including,” and “includes” mean including, but not limited to.